A Measurement-driven Analysis of Information Propagation in the Flickr Social Network author: Meeyoung Cha Alan Mislove Krishna P. Gummadi From Saarbrucken,

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Presentation transcript:

A Measurement-driven Analysis of Information Propagation in the Flickr Social Network author: Meeyoung Cha Alan Mislove Krishna P. Gummadi From Saarbrucken, Germany

Introduction The reason we study the information propagation in online social network – The popular social websites like twitter, facebook, flickr create networks of friends. – The interpersonal relationship, common interest create commercial needs, political needs etc. – So there are many analysis of “mouth-of-world influence”,”viral-marketing influence” in social network to see how the social network propagate information.

Introduction What the paper want to analysis? – How widely does information spread in the Flickr social network? – How quickly does information spread through the social network? – Does information in Flickr flow along its social network links?

Flickr Data Set Limitations – Do not take into account any deleted favorite marking – Do not know how a user was exposed to a photo prior to favorite-marking.

Network topology Maximum path length: 26 Average path length:5.67 ==>small-world network

Picture popularity Metric: number of views, fans, or comments Correlation coefficient=>ab:0.23,ac:0.13,bc:0.6 ab correlation coefficient over 100 fans: 0.21 ab correlation coefficient over 1000 fans:0.13 ==>This paper focus on fans

Local versus global picture popularity

Distance from fans to picture uploaders

Patterns of popularity growth

Long-term trends in popularity growth

Dissemination mechanisms People can find pictures through various mechanisms. – Featuring, search results, links between content, external links, Social network. Using a heuristic to infer the cases when Flickr users are likely to have discovered pictures using their friends links The way social network disseminate(heuristic)

The role of social cascades

Peer pressure in photo favorite marketing

Time taken for social cascade hops

Discussion Two possible reasons of high content locality – Models of viral marketing if r < 1 if r > 1 grow exp. – birds of a feather flock together One potential reason of delay in the social cascade – The frequency of users logins because the updates about their friends’ newly uploaded pictures.

Conclusion How picture popularity is distributed across the Flickr social network Finding differ from the common expectations about the quick and wide spread of word-of- mouth effect. Future work: Want developing tools and features that can enable full viral spread.